基于BSLA 最小二乘法的多功能传感器信号重构

最小二乘技术经常在利用若干测量数据评估被测变量的情况中使用，当使用这种方法来获得一个最佳线性逼近空间(BSLA)时，就会建立一个能够处理冗余度量数据的最佳解决方案，从而改善在三维空间环境中信号重构的效果(通常情况下，多功能传感器都是三元或三元以上的度量函数)。为此提出了一种应用最小二乘算法重构多功能传感器被测信号的方法验证其有效性，建立了仿真模拟电路，并给出了仿真结果和算法误差分析。

BSLA Based LMS Algorithm on Signal Reconstruction of Multifunctional Sensor

This paper proposes a novel strategy in signal reconstruction for multi functional sensors. Least mean squares technique has been used to realize the measurand evaluation from sparse calibration data. While using this method, one can obtain a best space for linearity approach (BSLA), in which an optimal solution is set up to process the redundant measurement data. It improves the measurement reconstruction under the circumstance of three dimension cases or even more, whereas the sensors often have three or more measurement functions and conventional techniques cease to be effective. To some extents, this research provides multi functional sensing with a general and practical way for data decomposition, and is more attractive in terms of computation simplicity and stable performance. An emulation circuit is also presented, as well as the simulation results and error analysis.